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Information and Entropy Econometrics — A Review and Synthesis

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  • Golan, Amos

Abstract

The overall objectives of this review and synthesis are to study the basics of information-theoretic methods in econometrics, to examine the connecting theme among these methods, and to provide a more detailed summary and synthesis of the sub-class of methods that treat the observed sample moments as stochastic. Within the above objectives, this review focuses on studying the inter-connection between information theory, estimation, and inference. To achieve these objectives, it provides a detailed survey of information-theoretic concepts and quantities used within econometrics. It also illustrates the use of these concepts and quantities within the subfield of information and entropy econometrics while paying special attention to the interpretation of these quantities. The relationships between information-theoretic estimators and traditional estimators are discussed throughout the survey. This synthesis shows that in many cases information-theoretic concepts can be incorporated within the traditional likelihood approach and provide additional insights into the data processing and the resulting inference.

Suggested Citation

  • Golan, Amos, 2008. "Information and Entropy Econometrics — A Review and Synthesis," Foundations and Trends(R) in Econometrics, now publishers, vol. 2(1–2), pages 1-145, February.
    • Handle: RePEc:now:fnteco:0800000004
    DOI: 10.1561/0800000004
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    Cited by:

    1. E. M. S. Ribeiro & G. A. Prataviera, 2014. "Information theoretic approach for accounting classification," Papers 1401.2954, arXiv.org, revised Sep 2014.
    2. Román Salmerón Gómez & Ainara Rodríguez Sánchez & Catalina García García & José García Pérez, 2020. "The VIF and MSE in Raise Regression," Mathematics, MDPI, vol. 8(4), pages 1-28, April.
    3. Andrew Friedson & Thomas Kniesner, 2012. "Losers and losers: Some demographics of medical malpractice tort reforms," Journal of Risk and Uncertainty, Springer, vol. 45(2), pages 115-133, October.
    4. Clara Cicatiello & Luca Secondi & Ludovica Principato, 2019. "Investigating Consumers’ Perception of Discounted Suboptimal Products at Retail Stores," Resources, MDPI, vol. 8(3), pages 1-10, July.
    5. Laureti, Tiziana & Secondi, Luca & Biggeri, Luigi, 2014. "Measuring the efficiency of teaching activities in Italian universities: An information theoretic approach," Economics of Education Review, Elsevier, vol. 42(C), pages 147-164.
    6. Luca Secondi, 2019. "Expiry Dates, Consumer Behavior, and Food Waste: How Would Italian Consumers React If There Were No Longer “Best Before” Labels?," Sustainability, MDPI, vol. 11(23), pages 1-15, December.
    7. Nancy McCarthy & Heath Henderson, 2014. "The Role of Renewable Energy Laws in Expanding Energy from Non-Traditional Renewables," IDB Publications (Working Papers) 86813, Inter-American Development Bank.
    8. Giuseppe Ragusa, 2011. "Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions," Econometric Reviews, Taylor & Francis Journals, vol. 30(4), pages 406-456, August.
    9. Ponta, Linda & Carbone, Anna, 2018. "Information measure for financial time series: Quantifying short-term market heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 132-144.
    10. Caticha, Ariel & Golan, Amos, 2014. "An entropic framework for modeling economies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 149-163.
    11. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.
    12. Heath Henderson, 2014. "Structural transformation and smallholder agriculture: an information-theoretic analysis of the Nicaraguan case," Agricultural Economics, International Association of Agricultural Economists, vol. 45(4), pages 443-458, July.
    13. Moriah B. Bostian & Cinzia Daraio & Rolf Fare & Shawna Grosskopf & Maria Grazia Izzo & Luca Leuzzi & Giancarlo Ruocco & William L. Weber, 2018. "Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach," DIAG Technical Reports 2018-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    14. Enrico Ciavolino & Antonio Calcagnì, 2014. "A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3401-3414, November.
    15. Yuri S. Popkov, 2021. "Qualitative Properties of Randomized Maximum Entropy Estimates of Probability Density Functions," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
    16. Rosa Bernardini Papalia & Esteban Fernandez-Vazquez, 2018. "Information theoretic methods in small domain estimation," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 347-359, April.
    17. Russ, Meir, 2016. "The probable foundations of sustainabilism: Information, energy and entropy based definition of capital, Homo Sustainabiliticus and the need for a “new gold”," Ecological Economics, Elsevier, vol. 130(C), pages 328-338.
    18. Henderson, Heath & Golan, Amos & Seabold, Skipper, 2015. "Incorporating prior information when true priors are unknown: An Information-Theoretic approach for increasing efficiency in estimation," Economics Letters, Elsevier, vol. 127(C), pages 1-5.
    19. McCarthy, Nancy & Henderson, Heath, 2014. "The Role of Renewable Energy Laws in Expanding Energy from Non-Traditional Renewables," IDB Publications (Working Papers) 6677, Inter-American Development Bank.
    20. Cinzia Daraio, 2017. "A framework for the Assessment of Research and its impacts," DIAG Technical Reports 2017-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

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